An Overhead-Optimizing Task Scheduling Strategy for Ad-hoc Based Mobile Edge Computing

Li, Tianze; Muqing, Wu; Zhao, Min; Wenxing, Liao

doi:10.1109/access.2017.2678102

Cited by 37 publications

(13 citation statements)

References 28 publications

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“…Table 3 and Figure , where β = {1, 5, 10, 15, 20, 30} and we can see that the average Ψ {i} and E [φ i ] are decline linearly and monotonically with β increasing, which also can obtain from equations (24) and(25). Moreover, the gradient of the average Ψ {i} and E [φ i ] are approximately equal.…”

supporting

confidence: 67%

“…Thus, they are suitable for multiobjective scenarios, especially for solving the optimization problem with economical cost and time cost as objectives. T. Li[25] et al formulate the task scheduling problem as a distributed multi-device task scheduling game. It is proved that the task scheduling game is a potential game, which possesses a property of finite improvement and always owns a Nash equilibrium.…”

mentioning

confidence: 99%

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The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

Wang

Dang

Zhao

et al. 2021

Preprint

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The emergence of multi-access edge computing (MEC) aims at extending cloud computing capabilities to the edge of the radio access network. As the large-scale IoT services are rapidly growing, a single edge infrastructure provider (EIP) may not be sufficient to handle the data traffic generated by these services. The coalition method has been used in MEC for resource optimization, latency, energy consumption reduction, computation offloading, etc. However, the majority of research does not consider the price of computing resources corresponded to a container. Moreover, each SP does not choose EIP with the highest cost-performance to sign a medium/long-term computing resource purchase or lease contract. In this work, we consider a scenario with a collection of SPs with different budgets and several EIPs distributed in geographical locations. During the first phase, we get the market equilibrium price and select the optimal EIPs to make a deal by solving the Eisenberg-Gale convex program. In the second stage, using a mathematical model, we maximize EIP's profits and form stable coalitions between EIPs by a distributed coalition formation algorithm. Numerical results demonstrate that the effectiveness of our method is significantly better than the existing model.

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confidence: 67%

mentioning

confidence: 99%

The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

Wang

Dang

Zhao

et al. 2021

Preprint

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“…Note that both energy consumption and delay are necessary to be considered in the process of offloading. Similar to [30], [31], in each group l, the non-negative weight factors α l and β l are introduced to tradeoff the energy consumption and delay. Therefore, in group l, the weighted sum of energy consumption and delay (WSED) in hybrid NOMA MEC systems is given by…”

Section: B Problem Formulationmentioning

confidence: 99%

Resource Allocation for Hybrid NOMA MEC Offloading

Zhu

Wang

Huang

et al. 2020

IEEE Trans. Wireless Commun.

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Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as promising technologies for the beyond fifth generation networks to achieve significant capacity improvement and delay reduction. In this paper, the technologies of hybrid NOMA and MEC are integrated. In the hybrid NOMA MEC system, multiple users are classified into different groups and each group is allocated a dedicated time slot. In each group, a user first offloads its task by sharing a time slot with another user, and then solely offloads during a time interval. To reduce the delay and save the energy consumption, we consider jointly optimizing the power and time allocation in each group as well as the user grouping. As the main contribution, the optimal power and time allocation is characterized in closed form. In addition, by incorporating the matching algorithm with the optimal power and time allocation, we propose a low complexity method to efficiently optimize user grouping. Simulation results demonstrate that the proposed resource allocation method in the hybrid NOMA MEC systems not only yields better performance than the conventional OMA scheme but also achieves quite close performance as global optimal solution.

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“…The problem of multi-user edge server selection in mobile edge computing has been extensively investigated in the past few years in many research tracks [7,9,[25][26][27]. These papers elaborated on how to select edge servers for multiple users from different aspects.…”

Section: Related Workmentioning

confidence: 99%

“…The total weighted response time over all jobs was minimized by the proposed approach. Rather than rely on remote cloud servers, the multi-device task scheduling strategy for the ad hoc-based mobile edge computing system was proposed in [26]. The authors developed a multi-device distributed task scheduling game, which can make the task be offloaded to an optimal mobile device.…”

Section: Related Workmentioning

confidence: 99%

Mobility-Enabled Edge Server Selection for Multi-User Composite Services

Zhang

et al. 2019

Future Internet

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In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user’s mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches.

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An Overhead-Optimizing Task Scheduling Strategy for Ad-hoc Based Mobile Edge Computing

Cited by 37 publications

References 28 publications

The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

Resource Allocation for Hybrid NOMA MEC Offloading

Mobility-Enabled Edge Server Selection for Multi-User Composite Services

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